The deep learning uncertainty perspective to characterize label noise inspires us to study label noise via deep learning uncertainty estimation techniques. Chen et al [1]} proposed using epistemic uncertainty estimation methods when learning with noisy labels. Comparing Monte Carlo Dropout, Bootstrap [2]}, Bayesian CNN upon Bayes by Backprop [3]} and certainty neural networks trained in noisy label settings, the authors discovered that Monte Carlo Dropout (MCDropout) had a prolonged memorization effect and possessed the best classification performance on test set.
